{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "PDR VGG19 Testing.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aldnoahh/plant-disease-recognition/blob/master/PDR_VGG19_Testing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QeWLiMnG0_uu",
        "colab_type": "text"
      },
      "source": [
        "#Plant Disease Recognition using VGG19 on modified version of PlantVillage Dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cj7pw9-JwAh3",
        "colab_type": "text"
      },
      "source": [
        "Importing necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RZFTFusEcsec",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d23d360e-d348-4d89-ec93-0f0c92a2c55e"
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.3.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a4s4g3_Bcwu8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.layers import Input, Dense, Flatten\n",
        "from tensorflow.keras.applications.vgg19 import VGG19 as PretrainedModel, preprocess_input\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import SGD, Adam\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "from glob import glob\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import sys, os"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zhXZ3Y9WwGdR",
        "colab_type": "text"
      },
      "source": [
        "Downloading and unzipping the modified dataset available on Gdrive. If you don`t have gdown module, install it using pip."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EPxnx4LVc_fW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!gdown --id 1Mj6wsKBZN2ycAyyIMs2lI361deuCJqBI --output pv0.zip\n",
        "!unzip pv0.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ngDbRT7iwdJi",
        "colab_type": "text"
      },
      "source": [
        "Check if the folder has been unzipped."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V7NhBksadZHd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "91d9a792-90cf-4d91-9f2f-46c420e79db1"
      },
      "source": [
        "!ls"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "pv0  pv0.zip  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jOLHCEL1wiUd",
        "colab_type": "text"
      },
      "source": [
        "Setting up path for datagenerators from keras"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xu1nf5xkeiXs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7b40de05-cffb-4a37-aa35-52981ee5cdd7"
      },
      "source": [
        "train_path = '/content/pv0/train'\n",
        "valid_path = '/content/pv0/test'\n",
        "# useful for getting number of files\n",
        "image_files = glob(train_path + '/*/*.JPG')\n",
        "valid_image_files = glob(valid_path + '/*/*.JPG')\n",
        "# useful for getting number of classes\n",
        "folders = glob(train_path + '/*')\n",
        "len(folders)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "38"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a-eZ_Jwnw22H",
        "colab_type": "text"
      },
      "source": [
        "Specify input image size."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pS1RfyFueujP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "IMAGE_SIZE = [256, 256]"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tt6ccnhhfJ6y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "03008094-70f0-4f06-e1bc-edb575e19521"
      },
      "source": [
        "#sneek peek at a random image\n",
        "plt.imshow(image.load_img(np.random.choice(image_files)))\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6g4APyRxBvP",
        "colab_type": "text"
      },
      "source": [
        "Configuring the pretrainned model as per our needs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mz1weoVCfN0-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "88a43cc0-c78a-449a-e75e-a164a4a2cfe8"
      },
      "source": [
        "ptm = PretrainedModel(\n",
        "    input_shape=IMAGE_SIZE + [3],\n",
        "    weights='imagenet',\n",
        "    include_top=False)\n",
        "# freeze pretrained model weights\n",
        "ptm.trainable = False"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "80142336/80134624 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YkxxS9zifoR3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "K = len(folders) # number of classes\n",
        "\n",
        "#model definition\n",
        "x = Flatten()(ptm.output)\n",
        "x= BatchNormalization()(x)\n",
        "x= Dense(512,activation='relu')(x)\n",
        "x = Dense(K, activation='softmax')(x)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TqzghjxNftgX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# create a model object\n",
        "model = Model(inputs=ptm.input, outputs=x)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U8EO7sLIft1r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "02c315a3-76ee-4d27-de22-24f5f2ad8616"
      },
      "source": [
        "# view the structure of the model\n",
        "model.summary()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 256, 256, 3)]     0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 256, 256, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 256, 256, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 128, 128, 64)      0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 128, 128, 128)     73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 128, 128, 128)     147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 64, 64, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 64, 64, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 64, 64, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 64, 64, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv4 (Conv2D)        (None, 64, 64, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 32, 32, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 32, 32, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 32, 32, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 32, 32, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv4 (Conv2D)        (None, 32, 32, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 16, 16, 512)       0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv4 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 8, 8, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 32768)             0         \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 32768)             131072    \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 512)               16777728  \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 38)                19494     \n",
            "=================================================================\n",
            "Total params: 36,952,678\n",
            "Trainable params: 16,862,758\n",
            "Non-trainable params: 20,089,920\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tYty5eS-U9gY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "de4983ee-2e12-4b49-8c74-369b63ebaae3"
      },
      "source": [
        "#view the number of layers in the model\n",
        "len(model.layers)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wKC5ilt4hc1K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# create an instance of ImageDataGenerator\n",
        "#Keras generators returns one-hot encoded labels and provides data augmentation.\n",
        "gen_train = ImageDataGenerator(\n",
        "  rotation_range=90,\n",
        "  width_shift_range=0.1,\n",
        "  height_shift_range=0.1,\n",
        "  shear_range=0.1,\n",
        "  zoom_range=0.2,\n",
        "  horizontal_flip=True,\n",
        "  preprocessing_function=preprocess_input\n",
        ")\n",
        "\n",
        "gen_test = ImageDataGenerator(\n",
        "  preprocessing_function=preprocess_input\n",
        ")"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SgtuBIoThuZc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "3230dbe1-52a2-4275-ed4f-7a9e004497dd"
      },
      "source": [
        "#batch size is the number of examples that are run through the model at once.\n",
        "batch_size = 300\n",
        "\n",
        "# create generators\n",
        "train_generator = gen_train.flow_from_directory(\n",
        "  train_path,\n",
        "  shuffle=True,\n",
        "  target_size=IMAGE_SIZE,\n",
        "  batch_size=batch_size,\n",
        ")\n",
        "valid_generator = gen_test.flow_from_directory(\n",
        "  valid_path,\n",
        "  target_size=IMAGE_SIZE,\n",
        "  batch_size=batch_size,\n",
        ")"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 46141 images belonging to 38 classes.\n",
            "Found 8162 images belonging to 38 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vXvEj-1byMah",
        "colab_type": "text"
      },
      "source": [
        "Since Keras no longer provides some metrics within itself, so we define those metrics ourselves. Here, we are defining F1_score, Precision and Recall."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vRsYGkdM3cTR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras import backend as Ke\n",
        "\n",
        "def recall_m(y_true, y_pred):\n",
        "    true_positives = Ke.sum(Ke.round(Ke.clip(y_true * y_pred, 0, 1)))\n",
        "    possible_positives = Ke.sum(Ke.round(Ke.clip(y_true, 0, 1)))\n",
        "    recall = true_positives / (possible_positives + Ke.epsilon())\n",
        "    return recall\n",
        "\n",
        "def precision_m(y_true, y_pred):\n",
        "    true_positives = Ke.sum(Ke.round(Ke.clip(y_true * y_pred, 0, 1)))\n",
        "    predicted_positives = Ke.sum(Ke.round(Ke.clip(y_pred, 0, 1)))\n",
        "    precision = true_positives / (predicted_positives + Ke.epsilon())\n",
        "    return precision\n",
        "\n",
        "def f1_m(y_true, y_pred):\n",
        "    precision = precision_m(y_true, y_pred)\n",
        "    recall = recall_m(y_true, y_pred)\n",
        "    return 2*((precision*recall)/(precision+recall+Ke.epsilon()))"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3LvAj8U_yg7Y",
        "colab_type": "text"
      },
      "source": [
        "This block is for creating a lr scheduler, since the lr scheduler was not as effective as using adam directly, it is left for experimentation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j0MqLWki7xxg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# from keras.optimizers import SGD\n",
        "# import math\n",
        "# def step_decay(epoch):\n",
        "# \tinitial_lrate = 1e-4\n",
        "# \tdrop = 0.5\n",
        "# \tepochs_drop = 10.0\n",
        "# \tlrate = initial_lrate * math.pow(drop, math.floor((1+epoch)/epochs_drop))\n",
        "# \treturn lrate\n",
        "# sgd = SGD(lr=0.0, momentum=0.9)\n",
        "# # learning schedule callback\n",
        "# from keras.callbacks import LearningRateScheduler\n",
        "# lrate = LearningRateScheduler(step_decay)\n",
        "# callbacks_list = [lrate]"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a_JS4tg1y17w",
        "colab_type": "text"
      },
      "source": [
        "Compiling our model with loss, optimizer and metrics (including our custom defined ones)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I5pDP0IStBZp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(\n",
        "  loss='categorical_crossentropy',\n",
        "  optimizer='adam',\n",
        "  metrics=['accuracy',f1_m,precision_m, recall_m]\n",
        ")"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EaUJj4D0y_VU",
        "colab_type": "text"
      },
      "source": [
        "The fit function is called for starting our training. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rlwkypTAsqW2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "outputId": "16e56c64-18ac-41bd-97e5-5917e621ef0a"
      },
      "source": [
        "# fit the model\n",
        "r = model.fit(\n",
        "  train_generator,\n",
        "  validation_data=valid_generator,\n",
        "  epochs=5,\n",
        "  steps_per_epoch=int(np.ceil(len(image_files) / batch_size)),\n",
        "  validation_steps=int(np.ceil(len(valid_image_files) / batch_size)),\n",
        ")"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "150/150 [==============================] - 795s 5s/step - loss: 0.7822 - accuracy: 0.8552 - f1_m: 0.8575 - precision_m: 0.8668 - recall_m: 0.8489 - val_loss: 0.6398 - val_accuracy: 0.9083 - val_f1_m: 0.9091 - val_precision_m: 0.9116 - val_recall_m: 0.9065\n",
            "Epoch 2/5\n",
            "150/150 [==============================] - 768s 5s/step - loss: 0.3244 - accuracy: 0.9265 - f1_m: 0.9274 - precision_m: 0.9322 - recall_m: 0.9227 - val_loss: 0.2975 - val_accuracy: 0.9395 - val_f1_m: 0.9406 - val_precision_m: 0.9434 - val_recall_m: 0.9379\n",
            "Epoch 3/5\n",
            "150/150 [==============================] - 758s 5s/step - loss: 0.2359 - accuracy: 0.9396 - f1_m: 0.9411 - precision_m: 0.9459 - recall_m: 0.9364 - val_loss: 0.2293 - val_accuracy: 0.9496 - val_f1_m: 0.9502 - val_precision_m: 0.9531 - val_recall_m: 0.9474\n",
            "Epoch 4/5\n",
            "150/150 [==============================] - 755s 5s/step - loss: 0.1881 - accuracy: 0.9482 - f1_m: 0.9491 - precision_m: 0.9533 - recall_m: 0.9449 - val_loss: 0.1689 - val_accuracy: 0.9570 - val_f1_m: 0.9567 - val_precision_m: 0.9590 - val_recall_m: 0.9544\n",
            "Epoch 5/5\n",
            "150/150 [==============================] - 754s 5s/step - loss: 0.1611 - accuracy: 0.9543 - f1_m: 0.9547 - precision_m: 0.9588 - recall_m: 0.9507 - val_loss: 0.1994 - val_accuracy: 0.9543 - val_f1_m: 0.9552 - val_precision_m: 0.9576 - val_recall_m: 0.9528\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tM5UwYJXzLiL",
        "colab_type": "text"
      },
      "source": [
        "Saving our Model in HD5 format."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dOhj9StKbLPC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1befb466-c9d5-4648-ebde-5b8ef64cdf17"
      },
      "source": [
        "model.save(\"model.h5\")\n",
        "print(\"Saved model to disk\")"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saved model to disk\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tL1gKZi_zSz2",
        "colab_type": "text"
      },
      "source": [
        "Graphs for our metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2oa3tHhOSC8J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "ea399085-8da8-4b2e-9eb0-50a224103948"
      },
      "source": [
        "# loss\n",
        "plt.plot(r.history['loss'], label='train loss')\n",
        "plt.plot(r.history['val_loss'], label='val loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T3urB6c6SDaU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "1a622628-a661-466b-80e5-51f5a791e891"
      },
      "source": [
        "# accuracies\n",
        "plt.plot(r.history['accuracy'], label='train acc')\n",
        "plt.plot(r.history['val_accuracy'], label='val acc')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-yt89FJw-_6M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "outputId": "383e7950-637e-430e-cc9c-f1186e1cb6f8"
      },
      "source": [
        "# f1_score\n",
        "plt.plot(r.history['f1_m'], label='train f1_m')\n",
        "plt.plot(r.history['val_f1_m'], label='val f1_m')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KxkOwoIHFXJa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "458db39a-217d-4be8-f062-05f242d50ce6"
      },
      "source": [
        "# precision\n",
        "plt.plot(r.history['precision_m'], label='train precision_m')\n",
        "plt.plot(r.history['val_precision_m'], label='val precision_m')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gd_sJAJ2FX0S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "75ed7d3f-5cb1-477e-e187-f00effd2b4ca"
      },
      "source": [
        "# recall\n",
        "plt.plot(r.history['recall_m'], label='train recall_m')\n",
        "plt.plot(r.history['val_recall_m'], label='val recall_m')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oATCaclAzgYy",
        "colab_type": "text"
      },
      "source": [
        "Next we evaluate the model on our test set again."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "KXJFLjk8tNad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "fc163ec7-3cae-4fdc-b726-88c513ad134c"
      },
      "source": [
        "# evaluate the model\n",
        "valid_generator = gen_test.flow_from_directory(valid_path,target_size=IMAGE_SIZE,batch_size=batch_size,)\n",
        "loss, accuracy, f1_score, precision, recall = model.evaluate(valid_generator, steps=int(np.ceil(len(valid_image_files)/ batch_size)))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 8162 images belonging to 38 classes.\n",
            "27/27 [==============================] - 57s 2s/step - loss: 0.1991 - accuracy: 0.9547 - f1_m: 0.9554 - precision_m: 0.9577 - recall_m: 0.9531\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x7bbYfpCzoDw",
        "colab_type": "text"
      },
      "source": [
        "Printing our metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "IB5Lnaf7tNak",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "9b737228-e46e-498f-e105-211a632ff1e1"
      },
      "source": [
        "print('loss     : ',loss)\n",
        "print('accuracy : ',accuracy)\n",
        "print('f1_score :',f1_score)\n",
        "print('precision:',precision)\n",
        "print('recall   :',recall)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "loss     :  0.19907443225383759\n",
            "accuracy :  0.9546913504600525\n",
            "f1_score : 0.9553820490837097\n",
            "precision: 0.9576932191848755\n",
            "recall   : 0.9530863761901855\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}